This reports stores the drafts of the visualizations generated from the combined data set produced by ./manipulation/4-combiner.R script.

Set the scene

Describe the working environment.

# Attach these packages so their functions don't need to be qualified
library(magrittr) # pipes
library(dplyr)    # disable when temp lines are removed
library(ggplot2)  # graphs
library(ggpubr)   # documents

# Call `base::source()` on any repo file that defines functions needed below. 
base::source("./scripts/common-functions.R")

path_file_input <- "./data-unshared/derived/9-combined.rds"
html_flip <- FALSE
baseSize <- 10


age_group_order <- c(
  "less_than_1"
  ,"1_4"        
  ,"5_9"        
  ,"10_14"      
  ,"15_19"      
  ,"20_24"      
  ,"25_34"      
  ,"35_44"      
  ,"45_54"      
  ,"55_64"      
  ,"65_74"      
  ,"75_84"      
  ,"85_plus"    
)

#Put code in here.  It doesn't call a chunk in the codebehind file.

dto      <- readRDS(path_file_input)
dto %>% pryr::object_size(); dto %>% class(); dto %>% names()

15.6 MB

[1] "list"

[1] "granularity_gls"        "granularity_population"

# to collapse into a single data frame
ds <- dto[["granularity_population"]] %>% 
  Reduce(function(a , b) dplyr::left_join( a, b ), . )

ds %>% explore::describe_all()

variable	type	na	na_pct	unique	min	mean	max
county	chr	0	0.0	67	NA	NA	NA
year	chr	0	0.0	17	NA	NA	NA
sex	chr	0	0.0	2	NA	NA	NA
race	chr	0	0.0	2	NA	NA	NA
ethnicity	chr	0	0.0	3	NA	NA	NA
age_group	chr	0	0.0	13	NA	NA	NA
population_count	dou	0	0.0	14511	0	2926.53	144791
resident_deaths	dou	97718	90.5	40	1	3.30	41
region	chr	101972	94.4	4	NA	NA	NA
community	dou	104156	96.4	35	8	491.19	3764
professionals	dou	102388	94.8	45	4	95.89	1161

# to help us filter out those counties that had programming
counties_gls <- ds %>% 
  distinct(county,region) %>% # those who have region had programming
  na.omit() %>%
  dplyr::distinct(county) %>% 
  as.list() %>% unlist() %>% as.character()


# to view the total programming delivered (between 2015 and 2017)
ds %>% 
  dplyr::filter(county %in% counties_gls) %>% 
  dplyr::distinct(county, year, community, professionals ) %>% 
  # na.omit() %>% 
  dplyr::group_by(county) %>% 
  dplyr::summarize(
    community      = sum(community, na.rm= T)
    ,professionals = sum(professionals, na.rm= T)
  ) %>% 
  dplyr::arrange(desc(professionals))

county	community	professionals
Orange	8182	1605
Saint Lucie	1068	830
Volusia	2170	432
Seminole	2400	419
Lake	206	290
Palm Beach	935	241
Duval	0	235
Saint Johns	250	210
Brevard	1126	166
Hernando	115	131
Marion	325	119
Indian River	245	118
Osceola	35	84
Sumter	85	81
Clay	0	53
Martin	535	46
Citrus	0	38
Flagler	397	29
Okeechobee	0	21
Putnam	0	13
Polk	0	11
Pasco	0	6
Leon	100	0

ds <- ds %>% 
  dplyr::rename(
    "deaths_by_suicide" = "resident_deaths" # to remind what we count
  ) %>% 
  dplyr::mutate(
    # to have a standardized measure / put counties on the same scale
    suicide_rate_per100k = (deaths_by_suicide / population_count) *100000
    # to have a single variable describing racial background
    ,racethnicity = paste0(race," + ", ethnicity)
    # to aid in graph production ( note the spaces at the end of "NE  ")
    ,rgn = car::recode(
      region,
      "
      'central'  ='CN'
      ;'southeast'='SE'
      ;'northeast'='NE  '
      "
    )
  ) %>% 
  
  dplyr::select(county, year, sex, age_group, race, ethnicity, racethnicity, # context
                region, rgn, # support for graphing and grouping
                population_count, deaths_by_suicide, suicide_rate_per100k, #measures
                community, professionals # treatment
  )

ds %>% explore::describe_all()

variable	type	na	na_pct	unique	min	mean	max
county	chr	0	0.0	67	NA	NA	NA
year	chr	0	0.0	17	NA	NA	NA
sex	chr	0	0.0	2	NA	NA	NA
age_group	chr	0	0.0	13	NA	NA	NA
race	chr	0	0.0	2	NA	NA	NA
ethnicity	chr	0	0.0	3	NA	NA	NA
racethnicity	chr	0	0.0	6	NA	NA	NA
region	chr	101972	94.4	4	NA	NA	NA
rgn	chr	101972	94.4	4	NA	NA	NA
population_count	dou	0	0.0	14511	0.00	2926.53	144791.00
deaths_by_suicide	dou	97718	90.5	40	1.00	3.30	41.00
suicide_rate_per100k	dou	97718	90.5	8395	0.83	63.70	7142.86
community	dou	104156	96.4	35	8.00	491.19	3764.00
professionals	dou	102388	94.8	45	4.00	95.89	1161.00

# to remind out how to aggregate 
# the most granular level includes (6):
# county, year, sex, age_group, race, ethnicity
d1 <- ds %>% 
  # apply filters to better understand the structure of the data
  # dplyr::filter(county     %in% c("Orange") ) %>%
  # dplyr::filter(year       %in% c("2015")   ) %>%
  # dplyr::filter(sex        %in% c("Male")   ) %>%
  # dplyr::filter(age_group  %in% c("15_19")  ) %>%
  # dplyr::filter(race       %in% c("White")  ) %>%
  # dplyr::filter(ethnicity %in% c("Non-Hispanic") )%>%
  # dplyr::filter(ethnicity %in% c("Hispanic","Non-Hispanic") )%>%
  dplyr::group_by(county, year, sex, age_group, race, ethnicity) %>% # no aggregation
  # to exemplify useful aggregates:
  # dplyr::group_by(county, year                               ) %>%
  # dplyr::group_by(county, year, sex                          ) %>%
  # dplyr::group_by(county, year, sex, age_group               ) %>%
  # dplyr::group_by(county, year, sex, racethnicity                  ) %>%
  dplyr::summarize(
    population_count   = sum(population_count,   na.rm = T)
    ,deaths_by_suicide = sum(deaths_by_suicide,  na.rm = T)
    ,professionals     = sum(professionals,      na.rm = T)
    ,community         = sum(community,          na.rm = T)
  ) 
# use the code for preparing data for custom graphs
# d1 %>% glimpse(60)
# d1 %>% explore::describe()

I Exposition

II. Development

Population Counts

p0

p1

p2

p3

p4

p5

p6

p7

Suicides Counts

Suicide Rates

III. Recapitulation

Session Information

For the sake of documentation and reproducibility, the current report was rendered in the following environment. Click the line below to expand.

Environment

- Session info -------------------------------------------------------------------------------------------------------
 setting  value                       
 version  R version 3.5.2 (2018-12-20)
 os       Windows 10 x64              
 system   x86_64, mingw32             
 ui       RTerm                       
 language (EN)                        
 collate  English_United States.1252  
 ctype    English_United States.1252  
 tz       America/Los_Angeles         
 date     2019-06-28                  

- Packages -----------------------------------------------------------------------------------------------------------
 package      * version  date       lib source        
 abind          1.4-5    2016-07-21 [1] CRAN (R 3.5.2)
 acepack        1.4.1    2016-10-29 [1] CRAN (R 3.5.3)
 assertthat     0.2.1    2019-03-21 [1] CRAN (R 3.5.3)
 backports      1.1.4    2019-04-10 [1] CRAN (R 3.5.3)
 base64enc      0.1-3    2015-07-28 [1] CRAN (R 3.5.2)
 callr          3.2.0    2019-03-15 [1] CRAN (R 3.5.3)
 car            3.0-3    2019-05-27 [1] CRAN (R 3.5.3)
 carData        3.0-2    2018-09-30 [1] CRAN (R 3.5.2)
 cellranger     1.1.0    2016-07-27 [1] CRAN (R 3.5.3)
 checkmate      1.9.3    2019-05-03 [1] CRAN (R 3.5.3)
 cli            1.1.0    2019-03-19 [1] CRAN (R 3.5.3)
 cluster        2.0.9    2019-05-01 [1] CRAN (R 3.5.3)
 codetools      0.2-16   2018-12-24 [1] CRAN (R 3.5.2)
 colorspace     1.4-1    2019-03-18 [1] CRAN (R 3.5.3)
 crayon         1.3.4    2017-09-16 [1] CRAN (R 3.5.3)
 curl           3.3      2019-01-10 [1] CRAN (R 3.5.3)
 data.table     1.12.2   2019-04-07 [1] CRAN (R 3.5.3)
 desc           1.2.0    2018-05-01 [1] CRAN (R 3.5.3)
 devtools       2.0.2    2019-04-08 [1] CRAN (R 3.5.3)
 digest         0.6.19   2019-05-20 [1] CRAN (R 3.5.3)
 dplyr        * 0.8.1    2019-05-14 [1] CRAN (R 3.5.3)
 DT             0.6      2019-05-09 [1] CRAN (R 3.5.3)
 evaluate       0.14     2019-05-28 [1] CRAN (R 3.5.2)
 explore        0.4.2    2019-05-22 [1] CRAN (R 3.5.3)
 forcats        0.4.0    2019-02-17 [1] CRAN (R 3.5.3)
 foreign        0.8-71   2018-07-20 [2] CRAN (R 3.5.2)
 Formula        1.2-3    2018-05-03 [1] CRAN (R 3.5.2)
 fs             1.3.1    2019-05-06 [1] CRAN (R 3.5.3)
 ggplot2      * 3.1.1    2019-04-07 [1] CRAN (R 3.5.3)
 ggpubr       * 0.2      2018-11-15 [1] CRAN (R 3.5.3)
 glue           1.3.1    2019-03-12 [1] CRAN (R 3.5.3)
 gridExtra      2.3      2017-09-09 [1] CRAN (R 3.5.3)
 gtable         0.3.0    2019-03-25 [1] CRAN (R 3.5.3)
 haven          2.1.0    2019-02-19 [1] CRAN (R 3.5.3)
 highr          0.8      2019-03-20 [1] CRAN (R 3.5.3)
 Hmisc          4.2-0    2019-01-26 [1] CRAN (R 3.5.3)
 hms            0.4.2    2018-03-10 [1] CRAN (R 3.5.3)
 htmlTable      1.13.1   2019-01-07 [1] CRAN (R 3.5.3)
 htmltools      0.3.6    2017-04-28 [1] CRAN (R 3.5.3)
 htmlwidgets    1.3      2018-09-30 [1] CRAN (R 3.5.3)
 httpuv         1.5.1    2019-04-05 [1] CRAN (R 3.5.3)
 knitr        * 1.23     2019-05-18 [1] CRAN (R 3.5.2)
 labeling       0.3      2014-08-23 [1] CRAN (R 3.5.2)
 later          0.8.0    2019-02-11 [1] CRAN (R 3.5.3)
 lattice        0.20-38  2018-11-04 [2] CRAN (R 3.5.2)
 latticeExtra   0.6-28   2016-02-09 [1] CRAN (R 3.5.3)
 lazyeval       0.2.2    2019-03-15 [1] CRAN (R 3.5.3)
 magrittr     * 1.5      2014-11-22 [1] CRAN (R 3.5.3)
 Matrix         1.2-17   2019-03-22 [1] CRAN (R 3.5.3)
 memoise        1.1.0    2017-04-21 [1] CRAN (R 3.5.3)
 mime           0.6      2018-10-05 [1] CRAN (R 3.5.2)
 munsell        0.5.0    2018-06-12 [1] CRAN (R 3.5.3)
 nnet           7.3-12   2016-02-02 [2] CRAN (R 3.5.2)
 openxlsx       4.1.0.1  2019-05-28 [1] CRAN (R 3.5.3)
 pillar         1.4.1    2019-05-28 [1] CRAN (R 3.5.2)
 pkgbuild       1.0.3    2019-03-20 [1] CRAN (R 3.5.3)
 pkgconfig      2.0.2    2018-08-16 [1] CRAN (R 3.5.3)
 pkgload        1.0.2    2018-10-29 [1] CRAN (R 3.5.3)
 plyr           1.8.4    2016-06-08 [1] CRAN (R 3.5.3)
 prettyunits    1.0.2    2015-07-13 [1] CRAN (R 3.5.3)
 processx       3.3.1    2019-05-08 [1] CRAN (R 3.5.2)
 promises       1.0.1    2018-04-13 [1] CRAN (R 3.5.3)
 pryr           0.1.4    2018-02-18 [1] CRAN (R 3.5.3)
 ps             1.3.0    2018-12-21 [1] CRAN (R 3.5.3)
 purrr          0.3.2    2019-03-15 [1] CRAN (R 3.5.3)
 R6             2.4.0    2019-02-14 [1] CRAN (R 3.5.3)
 RColorBrewer   1.1-2    2014-12-07 [1] CRAN (R 3.5.2)
 Rcpp           1.0.1    2019-03-17 [1] CRAN (R 3.5.3)
 readxl         1.3.1    2019-03-13 [1] CRAN (R 3.5.3)
 remotes        2.0.4    2019-04-10 [1] CRAN (R 3.5.3)
 reshape2       1.4.3    2017-12-11 [1] CRAN (R 3.5.3)
 rio            0.5.16   2018-11-26 [1] CRAN (R 3.5.3)
 rlang          0.3.4    2019-04-07 [1] CRAN (R 3.5.3)
 rmarkdown      1.13     2019-05-22 [1] CRAN (R 3.5.3)
 rpart          4.1-15   2019-04-12 [1] CRAN (R 3.5.3)
 rprojroot      1.3-2    2018-01-03 [1] CRAN (R 3.5.3)
 rstudioapi     0.10     2019-03-19 [1] CRAN (R 3.5.3)
 scales         1.0.0    2018-08-09 [1] CRAN (R 3.5.3)
 sessioninfo    1.1.1    2018-11-05 [1] CRAN (R 3.5.3)
 shiny          1.3.2    2019-04-22 [1] CRAN (R 3.5.2)
 stringi        1.4.3    2019-03-12 [1] CRAN (R 3.5.3)
 stringr        1.4.0    2019-02-10 [1] CRAN (R 3.5.3)
 survival       2.44-1.1 2019-04-01 [1] CRAN (R 3.5.3)
 tibble         2.1.3    2019-06-06 [1] CRAN (R 3.5.3)
 tidyselect     0.2.5    2018-10-11 [1] CRAN (R 3.5.3)
 usethis        1.5.0    2019-04-07 [1] CRAN (R 3.5.3)
 viridisLite    0.3.0    2018-02-01 [1] CRAN (R 3.5.3)
 withr          2.1.2    2018-03-15 [1] CRAN (R 3.5.3)
 xfun           0.7      2019-05-14 [1] CRAN (R 3.5.3)
 xtable         1.8-4    2019-04-21 [1] CRAN (R 3.5.3)
 yaml           2.2.0    2018-07-25 [1] CRAN (R 3.5.2)
 zip            2.0.2    2019-05-13 [1] CRAN (R 3.5.3)

[1] C:/Users/an499583/Documents/R/win-library/3.5
[2] C:/Program Files/R/R-3.5.2/library

Report rendered by an499583 at 2019-06-28, 10:48 -0700 in 73 seconds.

Counts + Rates